Typical commercialized petroleum reservoir visualization software helps petroleum and reservoir engineers and geoscientists see the results from static or dynamic simulations and visually compare iterative “what if” scenarios. Many reservoir models are often described as a disconnected curvilinear grid volume, also called a “3D grid,” where each grid cell has clearly defined hexahedronal geometry. The software shows different views of the reservoir with particular attributes (e.g. gas saturation) of the reservoir. The edges, top, and bottom of the reservoir can be seen by rotating the view.
Visualization can be used at four different points in the reservoir characterization and simulation process: 1) after gridding, 2) after initialization, 3) during simulation, and 4) after simulation. Visualization software typically allows the representation of any simulation attribute, instant switching between attributes, and the ability to set data thresholds with unique displays of cells that are restricted to specified data ranges. A visualization model may include a single layer, or multi-layer views wherein cells are stripped away to reveal the inside of the model. They can also be constructed to show a full display of corner points and local refinement for grid volumes. The traditional approach to setting up a modeling framework is a step-wise, process, not done in real-time, that employs two-dimensional (“2D”) interface dialog boxes with input fields and progress bars.
Geoscientists examine a variety of data types in the effort to find oil or gas. Seismic data is generally used to identify continuous reflections (representing horizons) and discontinuities (representing faults or other structural components) that form the structural framework of reservoirs containing hydrocarbons. This data type provides high resolution horizontal information, but lacks vertical detail. During oil and gas exploration, well data provides petrophysical and geological information from wire-line logs and cores. This well data contains high resolution vertical information, but lacks horizontal detail between wells. Sophisticated earth modeling tools integrate information from these two data types thus, optimizing both horizontal and vertical resolution. The result is a static model that can be used to build reservoir models to predict the oil and/or gas fluid flow and facilitate hydrocarbon production planning.
Visualization shows simulation effects on a specific area of the reservoir, represented in a model, through the use of 3D graphics objects. Simulation attributes display as color on the graphics objects and represent reservoir structure. As a result, physical changes in the reservoir, such as gas cap movement or pressure changes, can be easily evaluated. The ability to visualize a simulation model at any angle during the course of the simulation improves reservoir understanding.
A 3D reservoir model may be presented as hexahedral grid cells, which can be topologically structured or unstructured and geometrically regular or irregular. Curvilinear grid volumes, which are topologically structured and geometrically irregular, are more typical in reservoirs and are therefore, of particular interest. A 3D grid may be defined as:cell=f(I,J,K)=(v1,v2 . . . v8,a1,a2 . . . an)where v1, v2 . . . and v8 are eight vertices for the cell and a1, a2 . . . and an are attributes. 3D grids are I layers thick, J cells wide, K cells deep, which contain cells with coordinates (I, J, K) referred to as grid coordinates. Grid Coordinates (I, J, K) are typically used in an index domain, while Cartesian (world) coordinates (x, y, z) are typically used in a sampling domain.
Some commercial applications and research can visualize 3D grids and provide basic 3D scene interactive manipulations, such as rotation and zoom capabilities. However, 2D menus are used to define particular features, such as I, J, or K layers. For large or complicated volumes, image generation requires so much time that the software must display a progress bar. Although users can be trained to set parameters in 2D menus while working in 3D, they may become frustrated by this awkward interaction.
As referenced above, a 3D reservoir model is either topologically structured or unstructured, and volumes are geometrically regular or irregular. Unstructured volumes can easily be resampled to a regular structured volume using a rendering algorithm. Research for unstructured volume visualization includes the widely used Projected Tetrahedral technique. Many other extended and enhanced algorithms have also been published. Another algorithm used for visualizing geoscience data is incremental slicing, which was first introduced by Yagel, et al. in Hardware Assisted Volume Rendering of Unstructured Grids by Incremental Slicing, IEEE Visualization, 1996, pp. 55-62. The basic idea behind this algorithm is to slice the whole grid volume along the viewing direction and render the slices from back to front. For surface volume rendering, the well-known Marching Cubes algorithm can be used for rendering both regular and irregular grid cells. The challenge of volume visualization, however, lies in determining which algorithm best fits a particular domain and task.
Volume roaming (resizing or moving a region) is a common visualization technique used to focus on a dynamic subvolume of the entire data set in several oil and gas applications. GeoProbe®, which is a commercial-software package marketed by Landmark Graphics Corporation for use in the oil and gas industry, employs this basic technique using a sampling probe. The Geoprobe® sampling probe is described in U.S. Pat. No. 6,765,570, which is assigned to Landmark Graphics Corporation and is incorporated herein by reference. The sampling probe described in U.S. Pat. No. 6,765,570, however, only renders structured data (voxels) in real-time. In other words, the sampling probe does not address the need to render unstructured grids—much less geometrically irregular grid data—in real-time.
Although other publications (e.g. Speray and Kennon, in Volume Probes: Interactive Data Exploration on Arbitrary Grids, Computer Graphics, Vol. 24, No. 5 (November 1995, pp. 5-12)) describe a probe, none appear capable of rendering geometrically irregular grid data in real-time.
There is therefore, a need for imaging (rendering) 3D grids of geometrically irregular grid data in real-time.